beta_q0n <- genome_counts_filt %>%
column_to_rownames(., "genome") %>%
filter(rowSums(. != 0, na.rm = TRUE) > 0) %>%
dplyr::select_if(~!all(. == 0)) %>%
hillpair(., q = 0)
beta_q1n <- genome_counts_filt %>%
column_to_rownames(., "genome") %>%
filter(rowSums(. != 0, na.rm = TRUE) > 0) %>%
dplyr::select_if(~!all(. == 0)) %>%
hillpair(., q = 1)
beta_q1p <- genome_counts_filt %>%
column_to_rownames(., "genome") %>%
filter(rowSums(. != 0, na.rm = TRUE) > 0) %>%
dplyr::select_if(~!all(. == 0)) %>%
hillpair(., q = 1, tree = genome_tree)
beta_q1f <- genome_counts_filt %>%
column_to_rownames(., "genome") %>%
filter(rowSums(. != 0, na.rm = TRUE) > 0) %>%
dplyr::select_if(~!all(. == 0)) %>%
hillpair(., q = 1, dist = dist)
Permanova
#Richness
betadisper(beta_q0n$C, sample_metadata$Origin) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.00649 0.0064888 0.2231 999 0.637
Residuals 90 2.61727 0.0290808
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Feral Tame
Feral 0.634
Tame 0.63781
adonis2(beta_q0n$C ~ Origin,
data = sample_metadata %>% arrange(match(sample,labels(beta_q0n$C))),
permutations = 999,
strata = sample_metadata %>% arrange(match(sample,labels(beta_q0n$C))) %>% pull(Location)) %>%
broom::tidy() %>%
tt()
tinytable_oc1pusugf8pro2qj4xxj
| term |
df |
SumOfSqs |
R2 |
statistic |
p.value |
| Origin |
1 |
0.360299 |
0.01814891 |
1.663594 |
0.23 |
| Residual |
90 |
19.492078 |
0.98185109 |
NA |
NA |
| Total |
91 |
19.852377 |
1.00000000 |
NA |
NA |
#Neutral diversity
betadisper(beta_q1n$C, sample_metadata$Origin) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.01097 0.010969 0.5763 999 0.454
Residuals 90 1.71289 0.019032
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Feral Tame
Feral 0.456
Tame 0.44974
adonis2(beta_q1n$C ~ Origin,
data = sample_metadata %>% arrange(match(sample,labels(beta_q1n$C))),
permutations = 999,
strata = sample_metadata %>% arrange(match(sample,labels(beta_q1n$C))) %>% pull(Location)) %>%
broom::tidy() %>%
tt()
tinytable_85eatvrs49db0n998i4x
| term |
df |
SumOfSqs |
R2 |
statistic |
p.value |
| Origin |
1 |
0.3587603 |
0.01623493 |
1.485257 |
0.287 |
| Residual |
90 |
21.7392877 |
0.98376507 |
NA |
NA |
| Total |
91 |
22.0980479 |
1.00000000 |
NA |
NA |
#Phylogenetic diversity
betadisper(beta_q1p$C, sample_metadata$Origin) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.01235 0.012355 0.6076 999 0.444
Residuals 90 1.83012 0.020335
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Feral Tame
Feral 0.447
Tame 0.43775
adonis2(beta_q1p$C ~ Origin,
data = sample_metadata %>% arrange(match(sample,labels(beta_q1p$C))),
permutations = 999,
strata = sample_metadata %>% arrange(match(sample,labels(beta_q1p$C))) %>% pull(Location)) %>%
broom::tidy() %>%
tt()
tinytable_z5p632e1px0ejntch0xm
| term |
df |
SumOfSqs |
R2 |
statistic |
p.value |
| Origin |
1 |
0.1824935 |
0.02489257 |
2.297522 |
0.157 |
| Residual |
90 |
7.1487493 |
0.97510743 |
NA |
NA |
| Total |
91 |
7.3312427 |
1.00000000 |
NA |
NA |
#Functional diversity
betadisper(beta_q1f$C, sample_metadata$Origin) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.1982 0.198217 2.7199 999 0.089 .
Residuals 90 6.5589 0.072877
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Feral Tame
Feral 0.099
Tame 0.10259
adonis2(beta_q1f$C ~ Origin,
data = sample_metadata %>% arrange(match(sample,labels(beta_q1f$C))),
permutations = 999,
strata = sample_metadata %>% arrange(match(sample,labels(beta_q1f$C))) %>% pull(Location)) %>%
broom::tidy() %>%
tt()
tinytable_mtdrtn5tb3dqtkwfp2lm
| term |
df |
SumOfSqs |
R2 |
statistic |
p.value |
| Origin |
1 |
0.3017094 |
0.02299117 |
2.117898 |
0.244 |
| Residual |
90 |
12.8211288 |
0.97700883 |
NA |
NA |
| Total |
91 |
13.1228382 |
1.00000000 |
NA |
NA |
Richness diversity plot
beta_q0n$S %>%
vegan::metaMDS(., trymax = 500, k = 2, trace=0) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
dplyr::left_join(sample_metadata, by = join_by(sample == sample)) %>%
group_by(Origin,Location) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(aes(x = NMDS1, y = NMDS2, color = Origin, fill = Origin, shape = as.factor(Location))) +
scale_color_manual(name="Origin",
breaks=c("Tame","Feral"),
values=c("#6A9AC3","#F3B942")) +
scale_fill_manual(name="Origin",
breaks=c("Tame","Feral"),
values=c("#6A9AC350","#F3B94250")) +
geom_point(size = 4) +
# stat_ellipse(aes(color = beta_q1n_nmds$Groups))+
geom_segment(aes(x = x_cen, y = y_cen, xend = NMDS1, yend = NMDS2), alpha = 0.9) +
theme_classic() +
theme(
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 20, face = "bold"),
axis.text = element_text(face = "bold", size = 18),
panel.background = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size = 16),
legend.title = element_text(size = 18),
legend.position = "right", legend.box = "vertical"
) +
labs(shape="Individual")

Neutral diversity plot
beta_q1n$S %>%
vegan::metaMDS(., trymax = 500, k = 2, trace=0) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
dplyr::left_join(sample_metadata, by = join_by(sample == sample)) %>%
group_by(Origin,Location) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(aes(x = NMDS1, y = NMDS2, color = Origin, fill = Origin, shape = as.factor(Location))) +
scale_color_manual(name="Origin",
breaks=c("Tame","Feral"),
values=c("#6A9AC3","#F3B942")) +
scale_fill_manual(name="Origin",
breaks=c("Tame","Feral"),
values=c("#6A9AC350","#F3B94250")) +
geom_point(size = 4) +
# stat_ellipse(aes(color = beta_q1n_nmds$Groups))+
geom_segment(aes(x = x_cen, y = y_cen, xend = NMDS1, yend = NMDS2), alpha = 0.9) +
theme_classic() +
theme(
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 20, face = "bold"),
axis.text = element_text(face = "bold", size = 18),
panel.background = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size = 16),
legend.title = element_text(size = 18),
legend.position = "right", legend.box = "vertical"
) +
labs(shape="Individual")
